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Record W2901992650 · doi:10.1002/tcr.201800163

Recent Advances in Mechanically Robust and Stretchable Bulk Heterojunction Polymer Solar Cells

2018· review· en· W2901992650 on OpenAlexafffund
P. Blake J. St. Onge, Michael U. Ocheje, Mariia Selivanova, Simon Rondeau‐Gagné

Bibliographic record

VenueThe Chemical Record · 2018
Typereview
Languageen
FieldEngineering
TopicOrganic Electronics and Photovoltaics
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNanotechnologyConformable matrixOrganic solar cellPhotovoltaicsPhotovoltaic systemMaterials scienceHeterojunctionSolar energyOptoelectronicsEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Organic bulk heterojunction solar cells are promising candidates as future photovoltaic technologies for large-scale and low-cost energy production. It is, therefore, not surprising that research on the design and preparation of these types of organic photovoltaics has attracted a lot of attention since the last two decades, leading to constantly growing values of energy conversion and efficiency. Combined with the possibility of a large-scale production via roll-to-roll printing techniques, bulk heterojunction solar cells enable the fabrication of conformable, light-weight and flexible light-harvesting devices for point-of-use applications. This perspective review will highlight the recent advances toward mechanically robust and intrinsically stretchable bulk heterojunction solar cells. Mechanically robust fullerene-based and all-polymer devices will be presented, as well as a comprehensive overview of the recent challenges and characterization techniques recently developed to overcome some of the challenges of this research area, which is still in its infancy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.235
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations58
Published2018
Admission routes2
Has abstractyes

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